Multiple classification of the force and acceleration signals extracted during multiple machine processes: part 1 intelligent classification from an anomaly perspective

  • James M. Griffin
  • Alejandro J. Doberti
  • Valbort Hernández
  • Nicolás A. Miranda
  • Maximiliano A. Vélez


This paper is the first in a two-part work, where the investigation into the characteristics of multiple machine processes is made in order to accurately control them via the frequently used machine centre platform. The two machining processes under investigation are grinding and hole making: for grinding anomalies, grinding burn and chatter and for hole making, drilling, increased tool wear and onset of drill tool malfunction, which is also significant to severe scoring and material dragging. Most researchers usually report on one machining process as opposed to multiple which is less consistent with automated flexible systems where more than one machining process must be catered for. For efficient monitoring of automated multiple manufacturing processes, any unwanted anomalies should be identified and dealt with in a prompt and seamless manner. This first part provides two experimental set-ups (same set-up with tool interchange) to obtain signal signatures for both grinding and drilling phenomena (using the same material). Here, an approach based on neural networks and CARTs is used to reliably detect anomalies for both processes using a single acquisition path, opening the door for control implementation.


Burn Chatter Force Accelerations Drilling Tool malfunction Grinding CART Neural network STFT 


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  1. 1.
    Zelinski, P. (2005). How to perfect a machining process (Or at least how to make it more trustworthy). Modern Machine ShopGoogle Scholar
  2. 2.
    Liu Q, Chen X, Gindy N (2005) Fuzzy pattern recognition of AE signals for grinding burn. International Journal of Machine Tools & Manufacture 45(7–8):811–818CrossRefGoogle Scholar
  3. 3.
    Liu, Q. (2004). Pattern recognition of grinding defects and assessment strategies of grinding. Dissertation University of Nottingham. School of M3.Google Scholar
  4. 4.
    Pratap, S., Daultani, Y., Tiwari, M., & Mahanty, B. (2015). Rule based optimization for a bulk handling port operations. Journal of Intelligent manufacturing, 1–25.Google Scholar
  5. 5.
    Griffin J, Chen X (2009) Multiple classifications of the acoustic emission signals extracted during burn and chatter anomalies using genetic programming. International Journal of Advanced Manufacturing Technology 45(11–12):1152–1168CrossRefGoogle Scholar
  6. 6.
    Inasaki I, Karpuschewski B a (2001) Grinding chatter—origin and suppression. Cirp Annals-Manufacturing Technology 50(2):515–534CrossRefGoogle Scholar
  7. 7.
    Chen X, Limchimchol T (2006) Monitoring grinding wheel redress-life using support vector machines. International Journal of Automation and Computing 3(1):56–62CrossRefGoogle Scholar
  8. 8.
    Pour M (2015) Simultaneous application of time series analysis and wavelet transform for determining surface roughness of the ground workpieces. International Journal of Advanced Manufacturing Technology 85(5–8):1793–1805Google Scholar
  9. 9.
    Kaplonek W, Nadolny K, Królczyk G (2016) The use of focus-variation microscopy for the assessment of active surfaces of a new generation of coated abrasive tools. Measuring Science. Review 16(2):42–53Google Scholar
  10. 10.
    Dominguez J, Manson G, Marshall M (2016) Tool condition monitoring of ceramic inserted tools in high speed machining through image processing. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering 10(8):1391–1398Google Scholar
  11. 11.
    Bhuiyan M, Choudhury I, Dahari M, Nukman Y, Dawal S (2016) Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement 92:208–217CrossRefGoogle Scholar
  12. 12.
    Zhou C, Deng H, Chen G (2016) Study on methods of enhancing the quality, efficiency, and accuracy of pulsed laser profiling. Precision Engineering 45:143–152CrossRefGoogle Scholar
  13. 13.
    DeHart, A., & Murphy, D. (2004). Machine shopping: how to become a better-informed machine tool consumer. Retrieved from American Machinist:
  14. 14.
    Ulsoy AG, Koren Y (1993) Control of machining processes. Journal of dynamic systems, measurement and control 115:301–308CrossRefGoogle Scholar
  15. 15.
    Tonshoff HK, Fremuth T et al (1994) Process monitoring in grinding. Institute of Production Engineering and Machine Tools, HannoverGoogle Scholar
  16. 16.
    Griffin J, Chen X (2014) Real-time neural network classifications of characteristics from emitted acoustic emission during horizontal single grit scratch tests. Journal of Intelligent Manufacturing 1–17Google Scholar
  17. 17.
    Rowe WB, Chen X (1996) Grinding vibration detection using a neural network. Journal of Engineering Manufacture 210(4):349–352Google Scholar
  18. 18.
    Duda R et al (2001) Pattern classification. Wiley, New YorkMATHGoogle Scholar
  19. 19.
    Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. WadsworthGoogle Scholar
  20. 20.
    Hartigan J (1985) Statistical theory in clustering. Journal of classification 2:63–76MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Sick B (2002) On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mechanical Systems and Signal Processing 16:487–546CrossRefGoogle Scholar
  22. 22.
    Ozel T, Karpat Y (2005) Predictive modelling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools & Manufacture 45:467–479CrossRefGoogle Scholar
  23. 23.
    Mallet S (1999) A wavelet tour of signal processing. Academic Press, San DiegoGoogle Scholar
  24. 24.
    Xiaoli L, Yingxue Y, Zhejun Y (1997) On-line tool condition monitoring system with wavelet fuzzy neural network. Journal of Intelligent Manufacturing 8:271–276CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • James M. Griffin
    • 1
  • Alejandro J. Doberti
    • 2
  • Valbort Hernández
    • 2
  • Nicolás A. Miranda
    • 2
  • Maximiliano A. Vélez
    • 2
  1. 1.Mechanical Automotive and Manufacturing (MAM), Faculty of Engineering and ComputingCoventry UniversityCoventryUK
  2. 2.Department of Mechanical EngineeringUniversity of ChileSantiagoChile

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